{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time series outlier detection with Seq2Seq models on synthetic data\n",
    "\n",
    "## Method\n",
    "\n",
    "The [Sequence-to-Sequence](https://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf) (Seq2Seq) outlier detector consists of 2 main building blocks: an encoder and a decoder. The encoder consists of a [Bidirectional](https://en.wikipedia.org/wiki/Bidirectional_recurrent_neural_networks) [LSTM](https://colah.github.io/posts/2015-08-Understanding-LSTMs/) which processes the input sequence and initializes the decoder. The LSTM decoder then makes sequential predictions for the output sequence. In our case, the decoder aims to reconstruct the input sequence. If the input data cannot be reconstructed well, the reconstruction error is high and the data can be flagged as an outlier. The reconstruction error is measured as the mean squared error (MSE) between the input and the reconstructed instance. \n",
    "\n",
    "Since even for normal data the reconstruction error can be state-dependent, we add an outlier threshold estimator network to the Seq2Seq model. This network takes in the hidden state of the decoder at each timestep and predicts the estimated reconstruction error for normal data. As a result, the outlier threshold is not static and becomes a function of the model state. This is similar to [Park et al. (2017)](https://arxiv.org/pdf/1711.00614.pdf), but while they train the threshold estimator separately from the Seq2Seq model with a Support-Vector Regressor, we train a neural net regression network end-to-end with the Seq2Seq model.\n",
    "\n",
    "The detector is first trained on a batch of unlabeled, but normal (*inlier*) data. Unsupervised training is desireable since labeled data is often scarce. The Seq2Seq outlier detector is suitable for both **univariate and multivariate time series**.\n",
    "\n",
    "## Dataset\n",
    "\n",
    "We test the outlier detector on a synthetic dataset generated with the [TimeSynth](https://github.com/TimeSynth/TimeSynth) package. It allows you to generate a wide range of time series (e.g. pseudo-periodic, autoregressive or Gaussian Process generated signals) and noise types (white or red noise). It can be installed as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install git+https://github.com/TimeSynth/TimeSynth.git"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally, this notebook requires the `seaborn` package for visualization which can be installed via `pip`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"TF_USE_LEGACY_KERAS\"] = \"1\"\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, f1_score, recall_score\n",
    "import tensorflow as tf\n",
    "import timesynth as ts\n",
    "\n",
    "from alibi_detect.od import OutlierSeq2Seq\n",
    "from alibi_detect.utils.perturbation import inject_outlier_ts\n",
    "from alibi_detect.saving import save_detector, load_detector\n",
    "from alibi_detect.utils.visualize import plot_feature_outlier_ts, plot_roc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create multivariate time series\n",
    "\n",
    "Define number of sampled points and the type of simulated time series. We use [TimeSynth](https://github.com/TimeSynth/TimeSynth) to generate sinusoidal signals with noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_points = int(1e6)  # number of timesteps\n",
    "perc_train = 80  # percentage of instances used for training\n",
    "perc_threshold = 10  # percentage of instances used to determine threshold\n",
    "n_train = int(n_points * perc_train * .01)\n",
    "n_threshold = int(n_points * perc_threshold * .01)\n",
    "n_features = 2  # number of features in the time series\n",
    "seq_len = 50  # sequence length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(800000, 2) (100000, 2) (100000, 2)\n"
     ]
    }
   ],
   "source": [
    "# set random seed\n",
    "np.random.seed(0)\n",
    "\n",
    "# timestamps\n",
    "time_sampler = ts.TimeSampler(stop_time=n_points // 4)\n",
    "time_samples = time_sampler.sample_regular_time(num_points=n_points)\n",
    "\n",
    "# create time series\n",
    "ts1 = ts.TimeSeries(\n",
    "    signal_generator=ts.signals.Sinusoidal(frequency=0.25),\n",
    "    noise_generator=ts.noise.GaussianNoise(std=0.1)\n",
    ")\n",
    "samples1 = ts1.sample(time_samples)[0].reshape(-1, 1)\n",
    "\n",
    "ts2 = ts.TimeSeries(\n",
    "    signal_generator=ts.signals.Sinusoidal(frequency=0.15),\n",
    "    noise_generator=ts.noise.RedNoise(std=.7, tau=0.5)\n",
    ")\n",
    "samples2 = ts2.sample(time_samples)[0].reshape(-1, 1)\n",
    "\n",
    "# combine signals\n",
    "X = np.concatenate([samples1, samples2], axis=1).astype(np.float32)\n",
    "\n",
    "# split dataset into train, infer threshold and outlier detection sets\n",
    "X_train = X[:n_train]\n",
    "X_threshold = X[n_train:n_train+n_threshold]\n",
    "X_outlier = X[n_train+n_threshold:]\n",
    "\n",
    "# scale using the normal training data\n",
    "mu, sigma = X_train.mean(axis=0), X_train.std(axis=0)\n",
    "X_train = (X_train - mu) / sigma\n",
    "X_threshold = (X_threshold - mu) / sigma\n",
    "X_outlier = (X_outlier - mu) / sigma\n",
    "print(X_train.shape, X_threshold.shape, X_outlier.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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42Su1ukgyhBxr7qcwI5n05PgFvX5lXhrOpDjtfloAO05446KXV4Z3qqcWillwexRVzX2sLwre73fz5iLy0pL44csn9aoixMwWyJ4PIsLnrljBmc4hnqtqDZVpMU91S/+C4hN+7DbhwtJMHdBeANtPdLCuMJ2cIPtrLRQtFLNQ2zHI8LibdfPolZ8YZ+ezl5fzZl0Pu2v1hz6UnGzzDp5fsQjXE8C1a/Moz3bwP6/odOZQMDLu5kzn0ILdTn4uKsuktmOIjoGxEFlmfXqHxzlQ38OVYSqym4oWilmYrMieI5A9nVu2lJDjTOTHf64Jh1kxS037IBkp8eTMkvEULHab8JnLy6lq6ufVmoCj1zVBcKJtAI8KrrV4IPwXZP4LAs3c7KzpxKPgipXhjU+AFopZOdLUR0qCnfKc+V3BJsXb+cw7ynn9dBcvV7eFybrY41SbN+MpFO2o37+pkNy0RP57x6kQWBbbHGsOfgZFIPzfs9oO3Uk2WHYcb2dJSjwb5uEeXyhaKGbhcGMvawvTFzTP97Zty1iZ5+Qrjxyha1AvpReLUoqT7QOsWEQgeyqJcXY+fVk5u2u72V/fE5L3jFWqW/pxJsZRtGRxMxBy0xJxJNg53TEUIsusjcejeMWXFhuJmeNaKGbA5fZwtLmf9Quc5ZsYZ+eev9pI/8gEX3vsiPaFL5KuoXF6hycWFciezq1bSshIiecXr9aG7D1jkWMt/azMd2Jb5I+ViFCW46C2UwtFMBxp6qNraJwrIhCfAC0UM1LTPsiYyzNnoV0gVuWn8Y/vquT5o208sl/n7S8Gv996MTUU03EkxnHVylz2nNErivnQNzLB9uPtfPe549z8s9c5UN+zaLeTn/LsVO16CpLtJ9oRgXdUREYodPfYGfBXZM8nNXYmPnVZOS9Xt/PNJ46ytTyToiUpoTAv5vBPQKvMDY3ryc+qfCeP7G+kY2As7OmF0cyYy813nz3B66c7OdE2gFIQZxPWFqbzyUvL+PgloRk3Wp7j4MnDzYxOuEmKn7kJp8bLjhMdbCjKmLWdTajRQjEDhxv7cCbFsSxzcT/sdpvwXx/ewLU/2MmX/vcQD31q66KX6LFITdsgzqS4oGcxB4v/Svh4az85zshcmUUju2o6uf+1M2wpy+SLV1eyuXQJm4qXkJwQ2h/z8pxUlIK6riFWLqDBYKzQNTjGocZe/v6qiogd01DXk4jcLyLtIlI1y/MiIj8SkVMiclhEzo+EXUea+lhXmB6SH/XizBS+cf0adtd2c/9rZ0JgXexR0z4Qsoynqfhz//Xs7cDsru0iwW7j15/YwuevquDi5dkhFwmAct88ilod0A7IzpoOlCJs0+xmwugYxQPAtQGevw6o8N3uAP4n3AaNudxUt/QvKj4xnZs3F3H1qly+9/wJTrTqPPH5cqp9cFGtO2ZjiSOBvLQkqlv03yQQu2u72ViSEXZ3kH/o0el2HacIxI4THWQ5EuZVDLxYDBUKpdROIFAJ843Ar5WX3UCGiIR1jNzJ1kEm3GrO1uLzQUT4zk3rcCbG8YXfH2Tc5QnZe1ud7qFxOgfHQxrInsrKfKdeUQSgb2SCo819bC3PCvuxUhLiyE9P0plPAegdHueFo21cvSo3om5so1cUc1EINEx53Ojb9jZE5A4R2Ssiezs6Ft7q+3CTP5AdWrXOTk3k3z+4juqWfh7d3xjS97YyNZMZT6FfUYDX/XSqfVCL9yzsrevGo2Crr8NruCnPcejMpwA8tKeekQk3t19cGtHjml0ogkYpda9SarNSanNOzsIDk0ca+1iSEr/oAqKZeNfqXHLTEnnttO6SGSyhaAYYiFX5abg8ajKzSvNW/PGJ80uWROR4y3NSqe0Y0rVHMzDu8vCr1+u4ZEXWopowLgSzC0UTUDzlcZFvW9g43NjHuqKMkAdOweuC2lqexV9Od+kvQpCcah/EkWAnPz08Q+NX53tXKtr9NDORik/4Kc92MDDmokN3NHgbzxxpoa1/jE9eGpp05PlgdqF4AviYL/tpK9CnlGoJ18FGJ9ycaBtYcEV2MGwrz6JzcEy3KgiSk20DrMh1hkW4AUqzHCTG2bRQzIA/PrEtAvEJP+d6Punvx1SUUvxiVy3lOQ6uqIxctpMfo9NjHwb+ApwnIo0i8kkR+ayIfNa3yzNALXAKuA/4XDjtOdbSj9ujQprxNB1/UPAvtdr9FAyLGX8aDHF2G5W5To7rbLS3cS4+EUmh0CmyM7HnTDdVTf184pIyQ2qxDC24U0rdOsfzCrgrQuZwZIGtxefDsqwU8tOT2F3bxW1bl4XtOFagd3icjoExKsOU8eRnVb6Tl6vbUUqFbeUSjeyu7SIhzsamkvB3J/VTkJ5MUrxNB7Sn8ctdZ8hIieem84sMOb7ZXU8R5XBjH9mpieSlhccfDufiFG/U6jjFXJyaDGSHJ+PJz6r8NLqGxvXQnGnsru1mU3Hk4hMANptQmqWbA06lrnOIF6vb+OhFy8JS6BgMWiimcKSpl/VF6WG/qvTGKcZ1ps0c+DOeVoTR9QTnKrSP6TjFJJGsn5iON/NJfzf8PPB6HXE24WPbjPNAaKHwMe7y0DM8EZFqRx2nCI6TbQMkx9spzAh9qvJUVuX5W3noOIUfI+ITfspzHDT0jOjaFryC/Ye9DVy/oYClYfR0zIUWCh8JcTb2fO0q7rpyRdiPVZyZTGFGMru1UATkVPsgFbmpYQ/epafEU5CexPFWvaLwY0R8wk95jgO3R1Hfrd1Pv9tTz/C425CU2KlooZiCiJAQF/7/EhHhovJMdtd24/HoOMVs1LQNht3t5GdVfppOkZ2CEfEJP+XZ3r95rKeQT7i9BXbbyrNYUxC5vk4zoYXCILaVZ9E9ND7ph9e8lf7RCVr7R8MeyPazKj+N0x1DjE64I3I8M2NkfAJ0iqyfZ6taae4bNXw1AVooDGMyTnG602BLzMmpMLfumM7KfCdu3coDMDY+AeBMiifHmRjzAe1f7jpDWbaDd66MfIHddLRQGERxZgpFS5LZXRuoeW7sUhOG8aeB0JlP5zAyPuFneY6D0zEsFH3DExxq6OVDFxSZYtiZFgoD2Vqexe4zXTpOMQPVLQMkxtkiNj62NMtBUryN4zrzydD4hJ/ynNSYrqVo6BkGzg1zMhotFAayrTyL3uEJTrTpH6epeDyK56pauawiG3uErqbsNuG8PB3QNjo+4ac820Hv8ATdQ+OG2mEUjT0jABG7UJoLLRQGsnW5P06h02Snsqeum9b+Ua7fUBDR467Kc1Ld2h/TFfNGxyf8LJ9sDhib7qdG34oiHOMOFoIWCgMpzEimJDNF11NM44lDzSTH27lmdW5Ej7sqP43eYW+2VaxihvgE6Mynxp4RUhPjyEiJN9oUQAuF4Wwtz+SNM7qews+E28OzR1q4ZnUuKQmR7VnpD2jHcpzCDPEJ8LpcEuw2TnfG6opihKIlyaZpUqmFwmC2Lc+ib2SCal0VDMCumk56hie4IcJuJ/CmyELsZj6ZJT4B3pjRsqyUGF5RDJvG7QRaKAznXD2Fdj8BPH6wifTkeN5RufBxtgslLck7AjdWA9pmiU/4idX52UopmnpGTBPIBi0UhpOfnkxpVoqupwBGxt28cKyN96zLi0grlZlYGcOZTyfbvD/K4RzcNR/Kc1Kp7x7G5Y6t5oD9Iy4Gxlx6RaF5K9uWZ/HGmS7cMR6nePl4G8Pj7ohnO01ldb6TM52x2cqjqXeYjJR4UhMNnWc2SXm2gwm3osGXKhorNJgs4wm0UJiCreVZDIy6YvZK1s8TB5tZ6kzkojLjXB+r8tPwKG+L81ijuXeUgnTz/DiVx2iK7LnUWO160kxBxym8gdQdJzp43/qCiBXZzYQ/8ykWRbupZ4RCE13FLo/RFNlzxXbm+VtooTABuWlJlGSmcLCh12hTDOP5qlbG3R5u3Gic2wmgJDOFlAR7zA0xUkrR1DsS9iFR8yEjJYFMRwK1MZYi66+hSE82Rw0FaKEwDZW5zph0d/h54lAzy7JSWG9wINVmE87Lc8Zcimz/qIvBMZephAL8zQFjbUUxbKoaCghSKETkUhH5G9/9HBExvkG6xajITeVM51BMjn9sHxjl9dOd3LChwBRfDv8Qo1gqgmzyuTsKTCYU5dmpMel6MlN8AoIQChH5BvAV4Ku+TfHAb0NxcBG5VkROiMgpEbl7huc/LiIdInLQd/tUKI5rRipzU3F5FHVdsfWlAHjmcAsehSFFdjOxqTiDgVFXTA2Vau71CoWZYhTgraXoHByjb2TCaFMiglJqsirbTASzovgAcAMwBKCUagYWPXZMROzAT4HrgNXArSKyeoZdf6+U2ui7/WKxxzUr/kluseh+evxQM6vy06jIjcw0u7nwJxfEUg+upl7/iiLJYEveSqxlPvWNTDBoshoKCE4oxpW3naYCEJFQNUjfApxSStUqpcaB3wE3hui9o44VS1OxiXdOdCzR0D3Mgfpe06wmwDtUqjAjOaaEorl3hIQ4G9mORKNNeQux1hzQbO3F/QQjFH8QkZ8DGSLyaeAl4L4QHLsQaJjyuNG3bTo3ichhEfmjiBTP9mYicoeI7BWRvR0dHSEwL7IkxdspyUyhpj22VhRPHGoG4PoN+QZb8lYu8jVrjJWW4429IxSkJ5limtpUSjJTsNskZlyyDd3mK7aDIIRCKfV94I/AI8B5wNeVUj8Ot2E+ngRKlVLrgReBX822o1LqXqXUZqXU5pycyPcJCgUVuc7JNgqxwgtHWzm/JMN0V1Bby7PoHhqPmThFc6+5aij8xNttlGSmxMy0O/+Kothk34egsp6UUi8qpb6slPqSUurFEB27CZi6QijybZt63C6l1Jjv4S+AC0J0bFNSmZtKXQxlPo1OuDna3M+25eZoQjeVbTEWp2jqGTFVVfZUyrIdMeR6GsaZGEdasjnaqPgJJutpQET6fbdREXGLSCiSzN8EKkSkTEQSgFuAJ6Yde6o/4gagOgTHNS2VuU5cHsWZGLl6qmrqw+VRbCxeYrQpb6NoSXLMxCnGXG7aB8ZMuaIAr1DUdQ7FRLpyo6863gxp4lOZU7aUUpOpKOK1/kZg62IPrJRyicjfAs8DduB+pdRREfkWsFcp9QTweRG5AXAB3cDHF3tcMzM18+m8PHNkAIUTfyX6xmJjp6nNhIhwUXkmO050oJQy3Rc3lLT2eSf6ma2Gwk9ZtoORCTdtA6Pkm3TVEyoae0YoyTKX2wnmWZmtvPwJeHcoDq6UekYpVamUWq6U+jfftq/7RAKl1FeVUmuUUhuUUlcqpY6H4rhmpTzH4ct8io2A9oH6XoqWJJPjNFemjZ9YiVP4U2OLTCoU5dnezKczFnc/eWsozDWwyM+cKwoR+eCUhzZgMxC7Q4XDSFK8ndIsR8wEtA829Bo+mzkQU+MUlSap8QgH/qps07qe/CmynUNcvCLbYGvCR+/wBEPjbtMldkBwK4rrp9zeDQwQw/UO4aYiN5WTMZAi294/SlPvCJtKzBef8FO0JJmC9CTLxymae73XfXnp5iq285PrTCI53m752J0Zu8b6CSZG8TeRMETjpTLXyUvV7Yy53CTGGTvgPpwcMHF8wo+IsLU8i1dOWjtO0dQ7zFJnomk/bzabUJbtiAGhMGcNBQQQChH5Mb5q7JlQSn0+LBbFOBW5TtweRW3H0ORsBCtysKGXeLuwpsDc57i1PItHDzRxqn3QNC1GQk1T74hpA9l+ynIcHG3qM9qMsGLWqmwIvKLYGzErNJNU5np725xsG7C0UByo72F1fhpJ8ea8ivUzte+TVYWiuXeU1SYX7PJsB89VtTLu8hg2Tz3cNPQM40wy1xwKP7MKhVJq1ipoTfgoy3Zgt4mlez65PYrDjX3cfEGR0abMSXGmP07RzW3bSo02J+R4PN6BRdeszjXalICUZTtwexQNPcMs9zUKtBpmbC/uJ5ispxy8bcZXA5PRLqXUO8NoV8ySGGdnWVaKpbvInmwbYHjczUYTZzz5sXqcomtonHGXx3QDi6ZTNiVF1rpCMcyyrFD1XA0twazhHsRbEV0G/CtQh7eqWhMmKpc6LZ277y+022TCiuyZ2FqeRdfQOKcs+Dc51148SoTCogFt/xwKs/V48hOMUGQppX4JTActySAAACAASURBVCilXlFKfQLQq4kwUpmbytmuIUYn3EabEhYO1PewJCWeZSasQJ0JK8+nmBxYZHKhODc/25pC0TM8wfC425QZTxCcUPhHS7WIyHtFZBOQGUabYp6KXCceBactOqzlYEMvG4ozosaNU5yZTL4vTmE1JovtTC4UgC9F1prfCTOnxkJwQvFtEUkH/hH4Et4url8Mq1Uxjr8K2IqujoHRCWraB6PG7QTn4hRvnOmy3HyKpt4RUk3YrXQmyi1cS2Hm1FgITijeUEr1KaWqfP2WLvD3YtKEh7JsB3E2sWRA+3BjH0oRFYHsqWwtz6RzcNxyqzxvDUVSVKzuynIctPWPMTjmMtqUkONfUZi1jUowQvGaiLwgIp8Ukei5DIxiEuJslGZbs+fTZMfYomgTCm+c4i8Wcz81945EhdsJzjUHrLPgqqKxZ4Q0k9ZQQHAT7iqBfwHWAPtE5CkR+WjYLYtxKnNTLdlF9kB9D+U5DtJTzPmFmI2SzBRfnMJaAe1oqMr2U5btTYu1YkC7oXvYtG4nCH7C3R6l1D8AW/DOhdDFeGGmYqmTs93Dlsp8UkpxsKHX1P2dZmMyTlFrnTjF0JiL3uEJ07o7prMsKwURa7Yb9xbbmffvEMyEuzQRuV1EngVeB1rwCoYmjFTmOlHKWgHtxp4ROgfHTd0xNhAXlXnjFFa5oo2W1Fg/SfF2CtKTLZf5NFlDkRndK4pDwEbgW74hQ19RSu0Ls10xj7/nU42FWo4fmCy0i74VBXhbwAPUdw0bbEloaIoyoQDvcC+rZT51D40zMmHeGgoIooUHUK6sstaOIkqzHcTbxVIB7YP1vSTG2aJ2zKt/DGdz34jBloSGSaEw8Q/UdMqyHTx2oMlS7VTMnhoLwQWztUgYQLzdRlm2w1IB7QMNPawvSifeHp3dP5c6E7EJtPRaY8BjU88IcTZhqdOcA4tmojzbwcCoi66hcaNNCRlmHljkJzq/sTFCRa7TMiuKMZebo839URnI9hNnt5GblmSZFUVz7wh56UnYbdFzZV7mawhYa6GAttlrKEALhampXOqkoWeYkfHoz3yqbhlg3OVhYxRVZM9EfnqSdVYUUZQa66d8sjmgNS6gwLuiSE+OJy3JvCnjwWQ9VYrIyyJS5Xu8XkT+JfymaSpyUy2T+XSwvgeATVFWkT2d/IxkWiyzohilKMqEoiAjmQS7zTKZZ+BdUZjZ7QTBrSjuA76KrzmgUuowcEs4jdJ4mTrtLto52NDLUmci+enR4w+fiYL0JFr6RqO+lsLl9tDaPxp1Kwq7TViWlWKpWgqz11BAcEKRopTaM21bSJqtiMi1InJCRE6JyN0zPJ8oIr/3Pf+GiJSG4rjRwrIsX+aTBVJkD/gK7aI9UyU/PZkxl4fuKA+mtg2M4fYoU/vFZ6PMQs0B/TUUZs54guCEolNElgMKQEQ+hLfoblGIiB34KXAd3ul5t4rI6mm7fRLoUUqtAO4BvrvY40YT8XYb5dmpUT8WdWTczdmuYdYWphttyqIpyPCuiFr6ojtO4W8vHm0rCvA2BzzbNYzbE92rOvBOGByZcFNscsEORijuAn4OrBSRJuALwGdDcOwtwCmlVK1Sahz4HXDjtH1u5Fy7kD8CV0m0X5LOk/PynBxv6TfajEXhz9cvMXHlabBM1lL0RnecItqqsqdSnu1g3O2J+r8BQH23P+PJ3N+NgEIhIjZgs1LqaiAHWKmUulQpdTYExy4EGqY8bvRtm3EfpZQL6AOyZrH1DhHZKyJ7Ozo6QmCeOVhbmEZz32hUuzqiIf0vWPKtsqKYHIEafTGj8hzrNAc87OtWsKYgzWBLAhNQKJRSHuCffPeHlFKmdZYrpe5VSm1WSm3Oyckx2pyQsbbA66452txnsCULx/+jZPaAXTBkOxKJt0vU11I09Y6Q6UggJcH8A4um45+fXWuB2SD76nvJS0syvQswGNfTSyLyJREpFpFM/y0Ex24Ciqc8LvJtm3EfEYkD0gFr9XmegzU+oahqil73UzRWAM+GzSbkWaCWoqlnJCpXEwBZjgScSXGWCGjvP9vDBcvMX1sUjFD8Fd44xU5gn++2NwTHfhOoEJEyEUnAm3I7fXLeE8DtvvsfAv4cay1F0lPiKc5MpiqKVxSNPSPkZ0RXBXAg8tOjv5YimgYWTUdELDEWta1/lKbekaioLZpz3amUKgvHgZVSLhH5W+B5wA7cr5Q6KiLfAvb6xq3+EviNiJzCOwcjJus31hakU9UUvULR1DtCUYa5g3XzoSA9iTfreow2Y8EopWjqHeHSimyjTVkwZdmOqP4bgHc1AUTFimJOoRCRj820XSn168UeXCn1DPDMtG1fn3J/FLh5sceJdtYWpvNsVSt9IxOmHZUYiMaeYS6rsE7cKD8jmbb+FtweFZWrpL6RCYbH3VG7ogDvtLvHDzUzOuEmKd5utDkLYn99Dwlxtkn3spkJxvV04ZTbZcA3gRvCaJNmGv6MiGPN0RenGHd5aB8Ys0Qg209BehIuj6JzcMxoUxaEv1tpVAtFjgOl4GwUzwbZd7aH9YXpJMSZv+VeMG3G/27K7dPA+UBq+E3T+FkTxZlPLX0jKBXdP0rTifZaimicQzGdaG8OOOZyU9XUz/lR4HaChXWPHQLCErfQzEyOM5G8tKSojFNMXr1G8Y/SdKK9lqK5N3qrsv2U+oTidJT2fKpq6mfc7eH8KBkLHEyM4kl87TvwCstq4H/DaZTm7awtTKMqCl1P/lYRxSavPJ0PBdG+ougZISneRpYjwWhTFkxqYhzLslJ4/XQnd125wmhz5s0BXzfl85eZP+MJghuF+v0p913AWaVUY5js0czC2sJ0Xj7ezvC4K6qKpBp7hrEJ5EV519ipZKTEkxRvi9oVxZGmPpbnpEZ9g8abLyji+y+c5HTHIMtzossbvu9sD8WZyVFTWxSM6+k9SqlXfLfXlFKNIhJTzfnMwNqCdJSC6ijr+9TYO0JuWlLUjj+dCRGhIEprKYbGXOyv7+HSFdGbGuvnwxcWE28XHtxdb7Qp80Ipxf76nqhxO0FwQnHNDNuuC7UhmsD4O69GW4V2NPTaXwj5GUk0R2F19p4z3Uy4VVTXUPhZ6kzi3Wvy+OO+hqiaAtncN0pb/1hU1E/4mVUoROROETkCnCcih6fczgCHI2eiBiA3LZHs1ISoC2g39URvBXAgorU6+9WaThLibFxYGoouPMZz29Zl9I+6ePJQs9GmBM0+X6FdNK0oAjm7HwKeBf4DmDpUaEAp1R1WqzRvQ0RYU5AeVQFt/xQ1sw9lWQgF6Um0D4wx4fZElVtt16kOtpRmRm2R2nS2lGVSmZvKb3af5cMXFs/9AhOw/2wPyfF2VuY5jTYlaGb9hCul+pRSdUqpW31txUfwZj+likhJxCzUTLK2MI2atgFGJ6Jjmd3aPxq1U9TmoiAjGaW8/Xqihbb+UU62DVrC7eRHRLht6zKONPVxyNey2+zsr+9hQ3E6cVF0gTGnpSJyvYjUAGeAV4A6vCsNTYRZW5COy6M40Wrabu9vockCFcCzke87p2jKfNpV0wlgiUD2VN6/qZCUBDu/2R2KMTnhZWTczbHm/qhyO0FwwexvA1uBk74GgVcBu8NqlWZGJgPaUVKh7S+2s2Iwu8CX7htNtRS7TnWS5Uhgdb65h+TMF2dSPB/YVMiTh5rpMfmAr8ONvbg8KqoC2RCcUEwopboAm4jYlFLbgc1htkszA0VLkklLiouazKcmC1QAz0a0rSiUUuw61cnFK7KxRWEjw7n46NZljLk8/HGfuUu89td73WObLLii6BWRVOBV4EER+SHeNh6aCCMirC1Mj5qeT009I+Q4Ey0TOJ1KamIczqQ4WqJkRXGibYCOgTEus5jbyc+q/DQ2L1vCg2+cxeMx78ia/fU9lGc7yIyyqvhghOJGYBj4AvAccBq4PpxGaWZnbWE6x1sGmHB7jDZlThp7hy0Zn/BTkJ5Mc5SsKCbjExYKZE/ntm3LqOsaZtepTqNNmRGlFPvP9kTdagKC6x47hHcc6RVKqV8BvwDM7Qi0MGsK0hh3e6hpM3/XzKaeEUtmPPnJz0iKmlqKV2s6Kc9xWNIN6OfatXlkORJMG9Su7x6ma2g86uITEFzW06eBPwI/920qBP4UTqM0s7MuSgLaHo+iuXfUkoFsP/npyVExO3vM5eaNM12WdTv5SYyz8+ELi3m5us2USQaThXZR0ghwKsG4nu4CLgH6AZRSNcDScBqlmZ3SLAeOBDtHTV6h3TE4xrjbQ5GFr2AL0pPoGho3fV3LvrM9jE54uNRCUwZn46+3lKCAh/eYr//T/voenIlxVCyNnkI7P8EIxZhSatLVJCJxnGs7rokwNlt0VGhbcQ7FdPyZT60mj1PsqunEbhO2llujbUcgijNTuGR5Ns8fbTXalLex72wvG0syonJ8bjBC8YqIfA1IFpFr8M6ieDK8ZmkCsaYwjWPN/bhNnN3R2OMdUWnF9h1+JmspTB6n2HWqk03FGTiTom/e+kJYXZBGXdewqb4fg2MuTrRGX6Gdn2CE4m6gAzgCfAZ4BviXcBqlCczagnRGJtymHgM5OW7Twq6nyVoKE8cpeobGOdLUx2Ux4HbyU5btYNzlMVWc4nBDLx5F1Iw+nc6sTQFFpEQpVa+U8gD3+W4aEzC15fgKk/o7G3tGWJISjyMxeoYszZf8dP9IVPP8IE3n9dNdKGXttNjplE3O0x6iONMcK9oDvj5UG4ujL5ANgVcUk5lNIvJIKA8qIpki8qKI1Pj+nVFmRcQtIgd9tydCaUM0szzHQWKcjSMmDmhbPTUWICneTqYjwdS1FLtOdeBMimNDUbrRpkSM8hyvUNR2mGfFXdXUR2lWCunJ0en+CyQUUyMu5SE+7t3Ay0qpCuBl3trGfCojSqmNvtsNIbYhaomz21iVn2bq2RRNvdacQzGd/PQk01ZnK6V4taaTbeVZUdWpdLHkpCaSmhjHmU7zNJCoau5jTWH0inWgT4+a5X4ouBH4le/+r4D3h/j9Lc9aX0DbjO0KlFI09gxbOpDtxzvAyJwrirNdwzT2jHBZDLmdwNvqpizbQa1JhKJveIKG7hHWFlhTKDaISL+IDADrfff7RWRARBabm5mrlGrx3W8FcmfZL0lE9orIbhEJKCYicodv370dHR2LNM/8rMpPY2DMNRk0NhPdQ+OMTnhiYkVRkJFkqqDpVF495W/bETuBbD9l2Q7TrCj8vdnWFkZv195ZI41KqUV1chORl4C8GZ7652nHUSIy22XxMqVUk4iUA38WkSNKqdOz2HsvcC/A5s2bzXeZHWIqc71B7Jr2AdME7PxYub34dPLTk+kfdTE05jJd4H5XTQeFGcmUZpnr8xEJynMcPHm4mdEJt+FNKf1dFNZYdEWxKJRSVyul1s5wexxoE5F8AN+/7bO8R5Pv31pgB7ApXPZGG5W+bKeTJuz5NJkaGwNCUZBhzswnpRR763rYWp6FSPQVeC2WsmwHSnndb0ZT1dRPYUZy1HWMnYpREa4ngNt9928HHp++g4gsEZFE3/1svG1EjkXMQpOTnhLPUmciJ9vMN+3OP9muKMP6V7L56V4xbDZZLUVb/xhdQ+Osi2J3x2Ioz04FMEWtUVVzH2sKovvvYJRQfAe4xjdi9WrfY0Rks4j8wrfPKmCviBwCtgPfUUppoZhCZa7TlF1kG3uGcSbGkZZsLldMODBrLcU5v3j0ujsWQ2m29yLF6ID24JiLM51DUf93MOSb7JuYd9UM2/cCn/Ldfx1YF2HTooqK3FR+t6cBj0eZampZU6+3hiIWXB556UmImG9FUdXUj4g36SEWcSZ5V9y1HcYKRXVLP0pFdyAbjFtRaEJAZa6TkQn3ZPDYLDT2jMREIBsg3m4jJzXRdCuKquY+yrMdpguwRxIzZD75a52iOTUWtFBENZW5Xj+s2eIUTT2xUWznJz/DfLUUR5v6ot7dsVjKc8wgFP3kOBNZmpZkqB2LRQtFFOPv83Sy3TxC0TcywcCYKyYynvwUpJurlqJ7aJzmvtGoD6AulrJsB91D4/QOGzeQ82hzH2st8HfQQhHFpCfHk5eWZKqAdiy0F5+OvzpbKXOU70wGsqPc3bFYzmU+GbOqGJ1wU9M+aImVnRaKKKciN9VUrid/amwsuZ4KMpIYHnfTP+Iy2hTA6+6A6C7wCgVlk80BjRGK460DuD3KEn8HLRRRTmWuk1Ptg6YZ0hILk+2mM1lLYZKAdlVzH8WZyaSnRGen0lBRvCQFu00MW1H4uztHe8YTaKGIeipzUxlzeWjoNr4CFbypsUnxNrKiuAp1vuSbrDr7aFMfa/Kj/yp2sSTE2ShekmyYUBxt6iMjJd4Sq2stFFFORa6/lYc53E/+jKdYqKHw408Frm4x/m/QPzpBXdewJa5iQ0F5TqphRXdVzX2sLUi3xHdBC0WUU7HUG7CraTdHQLuxNzbai09lqTOJLWWZPPRGPS63x1Bbqpt98QkLBFBDgbeWYjDi7fjHXR5OtA6wxiKCrYUiynEmxVOQnmSuFUUMxSf8fOKSMpp6R3jxWJuhdlT5hCLWM578lGU7GJ3w0Nof2TqXk20DTLiVZf4OWigsQEWu0xRdZIfGXPQMT1jCJztfrlmdS9GSZP7fa3WG2nG0qY/ctERynImG2mEW/GNRIx2nsFqvLS0UFqAyN5XTHcZnPsXSHIrp2G3Cxy8uZU9dN0cajRtR6+1Uao0fp1Dgr6WIdJyiqqmf1MQ4lplsVsxC0UJhASpynYy7PJztMrZdwV9OeyeqrS/KMNQOo/jwhcU4Euz8v9fOGHL8kXE3p9oHLVEJHCpy0xJJjrdT2xHZFbe/tbiZmnUuBi0UFuC8XHMMMXr5eDvlOQ7Ksh2G2mEUaUnx3Ly5mCcPN9MeYZ84QHVrPx6lA9lT8c/PjqTryeX2UN3Sbxm3E2ihsAQr/JlPBga0B0Yn2F3bxdWrZht/Hht8/OJSXB7Fb3efjfixj/oD2Rb6gQoFkW4OWNs5xOiEx1IpylooLIAjMY6iJcmcNDBF9tWaTibciqtWLjXMBjNQmu3gqpW5/PaNekYn3BE9tr/AqyA9ujuVhprybAcN3cOMuyKTumyV1uJT0UJhEbzT7oxbUbx0rI305HguWLbEMBvMwicuLaV7aJwnDjZH9LhWKvAKJWU5DjwK6rsjs6qoauonKd5GeU5qRI4XCbRQWISK3FRqO4YMKfhyexTbT7Rz5Xk5xNn1R2pbeRYr85zc/9qZiHWUtVqBVygp82c+Rag5YFVzH6vz07BbJJANWigsQ+VSJ+NuD3Vdke/5tL++h57hCa5eHdvxCT8iwicuLeN46wB/qe2KyDGtVuAVSvzJFZGIU3g8imPN1gpkgxYKy1Dpy3wywv30UnUbcTbhHZU5ET+2WblhQwFZjgTu31UXkeMd87fu0KmxbyM9OZ7s1ISICMXZ7mEGx1yWE2wtFBZhxdJURIxJkX25up2LyjNJS4rtttZTSYq385Gty3j5eBt1EfiBqmruw5FgpzQrNlOT56Is2xGRojt/INtqLkAtFBYhOcFO8ZKUiI9FPds1xKn2Qa5aqd1O0/no1hLibMKDb4Q/VbaqyVuRbZUCr1BTlu2ISIyiqqmPBLuNCt+YYqughcJCVOamRtz19FJ1O0DM10/MxFJnEpuXZfLGme6wHsftURxr6bfcVWwoKc9JpXNwjP7RibAdw+X28ExVCxtLMkiIs9ZPqyFnIyI3i8hREfGIyOYA+10rIidE5JSI3B1JG6ORilwntR1DEcsXB3i5uo2KpamUZFmjp02o2ViSwbHm/rDWVNR2DHoLvCzmFw8l/oB2ON2Azx9to6F7hE9cUha2YxiFUbJXBXwQ2DnbDiJiB34KXAesBm4VkdWRMS86qcxNxeVR1EWo51PfyAR7znRzlV5NzMrG4gxcHjXZTTQcHJ2cQaFXFLNRHubMJ6UU9+48TWlWCtdYMPvPEKFQSlUrpU7MsdsW4JRSqlYpNQ78Drgx/NZFL36/aKRmU7xysgOXR3HN6tiuxg7EpmJvg8QD9b1hO0ZVUx+JcTZWWKjAK9SUZKVgEzgdpjjFm3U9HGrs45OXlVuqfsKPmR1phUDDlMeNvm0zIiJ3iMheEdnb0dERduPMyIqlqdgimPn0cnUbmY4ENhbrauzZWJqWRGFGMgcbwigUzX2szE/TxY4BSIyzU7QkJWwrint31pLpSOBD5xeF5f2NJmyfLBF5SUSqZriFZVWglLpXKbVZKbU5Jyc28/mT4u2UZKZEJKDtcnvYcaKDK89baskrqFCysTgjbCsKpRRHm/t1a/Eg8I9FDTWnOwZ5qbqN27YuIznBHvL3NwNx4XpjpdTVi3yLJqB4yuMi3zZNALzT7sIvFHvP9tA3MsHVq7TbaS42Fmfw9JEWOgbGQj557mhzPwOjLjYWx+YMkPlQlu1gz5luTncMsjyEbrpfvFpLYpyN27YtC9l7mg0zr1XfBCpEpExEEoBbgCcMtsn0VOamUtc1zJgrvJ1LX65uI8Fu4zJdjT0nm0q8P+LhcD/tOOFNT778PP13mIsPby4mOcHOjT95jWeOtITkPTsGxnhkfxM3XVBEdqp1x88alR77ARFpBLYBT4vI877tBSLyDIBSygX8LfA8UA38QSl11Ah7o4nKXCduj+J0e3gzn/zV2KmJYVuUWoa1henE2YSDDT0hf+/tJzpYV5jOUqduLT4XqwvSeOrvLqUiN5XPPbifbz91jIlFNtH8zV/qmHB7+OSl1kuJnYpRWU+PKaWKlFKJSqlcpdS7fdublVLvmbLfM0qpSqXUcqXUvxlha7RxYWkmIvDCsdawHaO2Y5DaziFdZBckSfF2VuY7Qx6n6B0e50B9D1fq1UTQFGQk8/s7tvHxi0v5xa4z/PV9u2lb4DTCkXE3v959lqtX5YbUlWVGzOx60iyAgoxktpVn8ej+prC1uPYv26/S8Ymg2VicweHGPtye0P1NdtZ04lFwRYwPi5ovCXE2vnnDGn506yaONvfz3h+9yl9Oz7/L7x/3NdA7PMFn3lEeBivNhRYKC3LT+UXUdw/zZl3oXR1uj+LhPQ1csiKLoiW6GjtYNhUvYXDMxemO0GXd7DjezpKUeDYU6UD2QrhhQwGP33UJ6cnxfPSXb8yrKNLtUfxi1xk2lWTExLAuLRQW5Nq1eaQk2HlkX2PI33vHiXaaekf46EXWzfAIBxv9Ae0QuZ88HsWOkx1cXpmj05MXQUWuk0fuvJh4u/Db3fVBv+6Fo62c7RrmjsvKY2KioBYKC+JIjOPatXk8faSFkfHQZj/9dvdZljoT9ZCieVKW5SAtKY4DIQpoH27qo3tonCu122nRZKQk8N51BTxxsImhMdec+yul+PnOWkoyU3jXmrwIWGg8WigsyofOL2JwzBXSoHZD9zA7TnZwy5YS4nUV8Lyw2YQNISy82368HRF4R4UOZIeCW7cUMzTu5qnDc885f7Ouh4MNvXzqsrKYWc3pb7tF2VqeRWFGMo/sD12N4kN76hG8XyrN/NlUsoSTbQNBXbXOxY6THWwszmCJIyEElmkuWLaEiqWpPLynYc597915mkxHAjdfEDvfAy0UFsVmEz6wqZBdNR0LTv+bypjLzR/ebOCqVbnkpyeHwMLYY1NxBh4FhxsX10m2c3CMw429XHmedjuFChHhli0lHGzopbqlf9b9atoGeKm6nY9ts267jpnQQmFhPnh+IR4Fjx1Y/KriuapWuobG+ehWHcReKBuKQ1OhvfNkB0qhhSLEfHBTIQl2G7/bM3tQ+96dtSTF2/jYttLIGWYCtFBYmPKcVM4vyeCRfY2Lrql4cHc9y7JSuGxFdoisiz0yHQksy0pZdIX29hMdZKcmskY3AgwpSxwJXLcuj8cONM04aKqtf5Q/HWziw5uLyYwxl58WCotz0wVF1LQPcqRp4e6OE60D7Knr5q+3lOiZzItkky+gvVDhdrk97DzZwRXn5ei/RRi45cIS+kddM/aCuv+1M7g9ik9dav0Cu+loobA471tXQEKcjUcXEdR+6I2zJMTZuHlz7ATvwsXG4gzaB8Zo6VtY3OhgQy99IxPa7RQmtpZnUpbt4OFp7qeB0Qke2l3PdevyY3LsrxYKi5OeEs81q3J5/GDTgmZpD425eHR/E+9dlx9zy+1wsLHEW8W70DjFjhMd2G3CpRXaBRgORIRbLizmzboeTrWfa9f/8J56BsZcMdGuYya0UMQAN11QSM/wBNt9LannwxOHmhkYc/HRrSVhsCz2WJXvJMFuW7BQbD/RzgXLlpCeHB9iyzR+brqgiHi78Dtfquy4y8P9u+rYVp7F+hhtl6KFIgZ4R0UO2amJ827poZTit7vPsjLPyfkl1u9nEwkS4+ysKUzjQP38A9pt/aMcbe7Xbqcwk52ayDWrc3lkfyNjLjdPHGqmtX+Uz1wem6sJ0EIRE8TZbbx/YwHbT7TTPTQe9OsONvRytLmfj25dFhP9bCLFxuIMjjT1zXsWwisnvLPgr1ypq7HDza1bSugZnuC5qlbu3XmalXlOLo/hIV1aKGKEmy4oYsKteHD32aBf8z87TuNIsPP+TYVhtCz22FicweiEhxOt8xtZu/1EO/npSZyX6wyTZRo/lyzPpjgzmf/7VDUn2wa54x2x0fxvNrRQxAir8tO4bm0eP95+ilPtc7e6fuZICy8ca+Oud67QU+xCzKbi+Qe0J9wedtV0csV5OTH9gxUpbDbhrzYX0zk4Rn56EtdvKDDaJEPRQhFD/OuNa0hJsPNPfzwUcIBOz9A4X3+8irWFadxxWez6ZcNFcWYyWY6EeTUIfLaqlYExF1fo+ETEuHlzMY4EO3desTzmm2DG9tnHGEudSXzj+tXsr+/lV6/Xzbrf/33qGL3DE3zvpg3ExfgXJByICOcvW8KOE+10Do7NuX/n4BjffOIo64vSuUq3FY8YuWlJ7PnnAF0gEAAACAZJREFUq7lNt63RQhFrvH9jIVeel8P3nj/O2a6htz2//Xg7jx5o4nNXLGe1bhERNr54dSUDYy7+4Q+H8MwxHvUbjx9lcNTF92/Wwh1pHIlx2tWHFoqYQ0T49w+uI95m4+5HjryllUT/6ARfe+wIlbmp3PXOFQZaaX1WF6Tx9fetZufJDn6+s3bW/Z4+3MLTR1r4+6srqNRBbI1BaKGIQfLTk/nae1fxl9qut/Tf/49njtPWP8r3PrSBxLjYaaFsFB+5qIT3rsvn+y+cYN/Z7rc93zk4xv95vIr1RekxWxGsMQeGCIWI3CwiR0XEIyKbA+xXJyJHROSgiOyNpI1W55YLi7lkRRb//kw1zb0jvH6qk4f31PPJS8vYWByb1aeRRkT4j5vWUZCRxN89dIDe4bfWuHz98SrtctKYAqM+fVXAB4GdQex7pVJqo1JqVkHRzB8R4TsfXI/bo/jKI4e5+9EjlGal8A/XnGe0aTFFWlI8P7n1fDoGx/jyHw9PugKfOtzMM0datctJYwoMEQqlVLVS6oQRx9acozgzha9cex6v1nRS3z3Md29aH1NTu8zChuIM7r5uFS8ea+OB1+voHBzj648f1S4njWkweyWVAl4QEQX8XCl172w7isgdwB0AJSW6gV2wfGxbKfvqe1me4+Ci8iyjzYlZPnFJKX853cW/P1PN04dbtMtJYypksZPPZn1jkZeAvBme+mel1OO+fXYAX1JKzRh/EJFCpVSTiCwFXgT+Tik1p7tq8+bNau9eHdLQRBe9w+O854ev0tw3ypfffR53XakzzzSRQ0T2zebiD9uKQil1dQjeo8n3b7uIPAZsIbi4hkYTdWSkJHDf7Zt5rqpVu5w0psK0ricRcQA2pdSA7/67gG8ZbJZGE1bWFKSzpiDdaDM0mrdgVHrsB0SkEdgGPC0iz/u2F4jIM77dcoFdInII2AM8rZR6zgh7NRqNJpYxZEWhlHoMeGyG7c3Ae3z3a4ENETZNo9FoNNPQKRUajUajCYgWCo1Go9EERAuFRqPRaAKihUKj0Wg0AdFCodFoNJqAaKHQaDQaTUDC1sLDSESkAzi7wJdnA50hNCda0OcdW+jzji2COe9lSqmcmZ6wpFAsBhHZG4stzfV5xxb6vGOLxZ63dj1pNBqNJiBaKDQajUYTEC0Ub2fWmRcWR593bKHPO7ZY1HnrGIVGo9FoAqJXFBqNRqMJiBYKjUaj0QREC4UPEblWRE6IyCkRudtoe8KJiNwvIu0iUjVlW6aIvCgiNb5/lxhpY6gRkWIR2S4ix0TkqIj8vW+7pc8bQESSRGSPiBzynfu/+raXicgbvs/870UkwWhbQ42I2EXkgIg85Xts+XMGEJE6ETkiIgdFZK9v24I/61oo8H6YgJ8C1wGrgVtFZLWxVoWVB4Brp227G3hZKVUBvOx7bCVcwD8qpVYDW4G7fH9jq583wBjwTqXUBmAjcK2IbAW+C9yjlFoB9ACfNNDGcPH3QPWUx7Fwzn6uVEptnFI/seDPuhYKL1uAU0qpWqXUOPA74EaDbQobSqmdQPe0zTcCv/Ld/xXw/ogaFWaUUi1Kqf2++wN4fzwKsfh5Aygvg76H8b6bAt4J/NG33XLnLiJFwHuBX/geCxY/5zlY8GddC4WXQqBhyuNG37ZYIlcp1eK734p3FK0lEZFSYBPwBjFy3j4XzEGgHXgROA30KqVcvl2s+Jn/AfBPgMf3OAvrn7MfBbwgIvtE5A7ftgV/1g0ZhaoxN0opJSKWzJsWkVTgEeALSql+70WmFyuft1LKDWwUkQy8Y4hXGmxSWBGR9wHtSql9InKF0fYYwKVKqSYRWQq8KCLHpz4538+6XlF4aQKKpzwu8m2LJdpEJB/A92+7wfaEHBGJxysSDyqlHvVttvx5T0Up1QtsB7YBGSLiv1i02mf+EuAGEanD60p+J/BDrH3Okyilmnz/tuO9MNjCIj7rWii8vAlU+DIiEoBbgCcMtinSPAHc7rt/O/C4gbaEHJ9/+pdAtVLq/5vylKXPG0BEcnwrCUQkGbgGb4xmO/Ah326WOnel1FeVUkVKqVK83+c/K6U+goXP2Y+IOETE6b8PvAuoYhGfdV2Z7UNE3oPXp2kH7ldK/ZvBJoUNEXkYuAJv6+E24BvAn4A/ACV4W7R/WCk1PeAdtYjIpcCrwBHO+ay/hjdOYdnzBhCR9XiDl3a8F4d/UEp9S0TK8V5tZwIHgI8qpcaMszQ8+FxPX1JKvS8Wztl3jo/5HsYBDyml/k1EsljgZ10LhUaj0WgCol1PGo1GowmIFgqNRqPRBEQLhUaj0WgCooVCo9FoNAHRQqHRaDSagOjKbI1mEfhSDl/2PcwD3ECH7/GwUupiQwzTaEKITo/VaEKEiHwTGFRKfd9oWzSaUKJdTxpNmBCRQd+/V4jIKyLyuIjUish3ROQjvhkRR0RkuW+/HBF5RETe9N0uMfYMNBovWig0msiwAfgssAq4DahUSm3B2wL773z7/BDvrIQLgZt8z2k0hqNjFBpNZHjT3+JZRE4DL/i2HwGu9N2/Glg9paNtmoikTpklodEYghYKjSYyTO0n5Jny2MO576EN2KqUGo2kYRrNXGjXk0ZjHl7gnBsKEdlooC0azSRaKDQa8/B5YLOIHBaRY3hjGhqN4ej0WI1Go9EERK8oNBqNRhMQLRQajUajCYgWCo1Go9EERAuFRqPRaAKihUKj0Wg0AdFCodFoNJqAaKHQaDQaTUD+f9I0MhPPnqW3AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_features = X.shape[-1]\n",
    "istart, istop = 50, 100\n",
    "for f in range(n_features):\n",
    "    plt.plot(X_train[istart:istop, f], label='X_train')\n",
    "    plt.title('Feature {}'.format(f))\n",
    "    plt.xlabel('Time')\n",
    "    plt.ylabel('Feature value')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load or define Seq2Seq outlier detector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "load_outlier_detector = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath = 'my_path'  # change to directory where model is saved\n",
    "if load_outlier_detector:  # load pretrained outlier detector\n",
    "    od = load_detector(filepath)\n",
    "else:  # define model, initialize, train and save outlier detector\n",
    "    \n",
    "    # initialize outlier detector\n",
    "    od = OutlierSeq2Seq(n_features,\n",
    "                        seq_len,\n",
    "                        threshold=None,\n",
    "                        latent_dim=100)\n",
    "    \n",
    "    # train\n",
    "    od.fit(X_train,\n",
    "           epochs=10,\n",
    "           verbose=False)\n",
    "    \n",
    "    # save the trained outlier detector\n",
    "    save_detector(od, filepath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We still need to set the outlier threshold. This can be done with the `infer_threshold` method. We need to pass a time series of instances and specify what percentage of those we consider to be normal via `threshold_perc`. First we create outliers by injecting noise in the time series via `inject_outlier_ts`. The noise can be regulated via the percentage of outliers (`perc_outlier`), the strength of the perturbation (`n_std`) and the minimum size of the noise perturbation (`min_std`). Let's assume we have some data which we know contains around 10% outliers in either of the features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100000, 2)\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "\n",
    "X_thr = X_threshold.copy()\n",
    "data = inject_outlier_ts(X_threshold, perc_outlier=10, perc_window=10, n_std=2., min_std=1.)\n",
    "X_threshold = data.data\n",
    "print(X_threshold.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize outlier data used to determine the threshold:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "istart, istop = 0, 50\n",
    "for f in range(n_features):\n",
    "    plt.plot(X_threshold[istart:istop, f], label='outliers')\n",
    "    plt.plot(X_thr[istart:istop, f], label='original')\n",
    "    plt.title('Feature {}'.format(f))\n",
    "    plt.xlabel('Time')\n",
    "    plt.ylabel('Feature value')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's infer the threshold. The ```inject_outlier_ts``` method distributes perturbations evenly across features. As a result, each feature contains about 5% outliers. We can either set the threshold over both features combined or determine a feature-wise threshold. Here we opt for the **feature-wise threshold**. This is for instance useful when different features have different variance or sensitivity to outliers. We also manually decrease the threshold a bit to increase the sensitivity of our detector:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New threshold: [[0.12968134 0.29728393]]\n"
     ]
    }
   ],
   "source": [
    "od.infer_threshold(X_threshold, threshold_perc=[95, 95])\n",
    "od.threshold -= .15\n",
    "print('New threshold: {}'.format(od.threshold))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's save the outlier detector with the updated threshold:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_detector(od, filepath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can load the same detector via `load_detector`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "od = load_detector(filepath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Detect outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate the outliers to detect:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100000, 2) (100000,)\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "\n",
    "X_out = X_outlier.copy()\n",
    "data = inject_outlier_ts(X_outlier, perc_outlier=10, perc_window=10, n_std=2., min_std=1.)\n",
    "X_outlier, y_outlier, labels = data.data, data.target.astype(int), data.target_names\n",
    "print(X_outlier.shape, y_outlier.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predict outliers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "od_preds = od.predict(X_outlier,\n",
    "                      outlier_type='instance',    # use 'feature' or 'instance' level\n",
    "                      return_feature_score=True,  # scores used to determine outliers\n",
    "                      return_instance_score=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display results\n",
    "\n",
    "F1 score, accuracy, recall and confusion matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score: 0.927 -- Accuracy: 0.986 -- Recall: 0.901\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = od_preds['data']['is_outlier']\n",
    "f1 = f1_score(y_outlier, y_pred)\n",
    "acc = accuracy_score(y_outlier, y_pred)\n",
    "rec = recall_score(y_outlier, y_pred)\n",
    "print('F1 score: {:.3f} -- Accuracy: {:.3f} -- Recall: {:.3f}'.format(f1, acc, rec))\n",
    "cm = confusion_matrix(y_outlier, y_pred)\n",
    "df_cm = pd.DataFrame(cm, index=labels, columns=labels)\n",
    "sns.heatmap(df_cm, annot=True, cbar=True, linewidths=.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the feature-wise outlier scores of the time series for each timestep vs. the outlier threshold:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_outlier_ts(od_preds,\n",
    "                        X_outlier, \n",
    "                        od.threshold[0],\n",
    "                        window=(150, 200),\n",
    "                        t=time_samples,\n",
    "                        X_orig=X_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot the ROC curve using the instance level outlier scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "roc_data = {'S2S': {'scores': od_preds['data']['instance_score'], 'labels': y_outlier}}\n",
    "plot_roc(roc_data)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
